There are many other works on abstractive summarization. We name a few here.
Abstractive summarizers are known to generate fluent summaries but be weak in retaining the main idea of source documents. Therefore, many researchers try to introduce a content selection mechanism so that the summary is generated conditioned on only the most important content selected.
[1]} extend the sequence-to-sequence framework with a selective mechanism that allows the decoder to generate a summary based on a selective portion of the source documents that is the most important to retain the main idea.
Similarly, [2]} propose an abstractive summarizer that first selects salient sentences and then rewrites them abstractively (via compression and paraphrasing) to generate a concise summary.
[3]} propose a data-efficient content selector to over-select phrases in a source document that should be part of the summary. The selector is used in a bottom-up attention step to constrain the model to using only the most likely phrases.
